Stability Analysis on Pattern-Based NN Control Systems
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This technical note introduces stability analysis on pattern-based neural network (NN) control systems. Firstly, different control situations are defined as dynamical patterns and are identified via deterministic learning (DL). When the dynamical pattern is correctly classified, the corresponding NN learning controller with knowledge or experience is selected. Secondly, by adopting a class of switching signals with average dwell time (ADT) property , it is shown that the NN learning controller can achieve small tracking errors and fast convergence rate with small control gains. These results will guarantee not only stability of the closed-loop systems, but also better performance in the aspects of time saving or energy saving. Finally, the theoretical analysis is supported by simulations.
KeywordsConvergence Pattern-based Average Dwell Time Deterministic Learning Uncertain Nonlinear System RBF Neural Network
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